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Features Importance

Spearman Correlation of Models

Summary of 5_Default_CatBoost
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CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 6
- rsm: 1
- loss_function: Logloss
- eval_metric: AUC
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
3.0 seconds
Metric details
|
score |
threshold |
| logloss |
0.600703 |
nan |
| auc |
0.738735 |
nan |
| f1 |
0.705256 |
0.372437 |
| accuracy |
0.68028 |
0.524116 |
| precision |
0.898734 |
0.833308 |
| recall |
1 |
0.0525236 |
| mcc |
0.361119 |
0.524116 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.600703 |
nan |
| auc |
0.738735 |
nan |
| f1 |
0.653165 |
0.524116 |
| accuracy |
0.68028 |
0.524116 |
| precision |
0.696356 |
0.524116 |
| recall |
0.615018 |
0.524116 |
| mcc |
0.361119 |
0.524116 |
Confusion matrix (at threshold=0.524116)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1300 |
450 |
| Labeled as 1 |
646 |
1032 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 1_Baseline
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Baseline Classifier (Baseline)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
0.3 seconds
Metric details
|
score |
threshold |
| logloss |
0.692927 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.657266 |
0.440414 |
| accuracy |
0.489498 |
0.440414 |
| precision |
0.489498 |
0.440414 |
| recall |
1 |
0.440414 |
| mcc |
0 |
0.440414 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.692927 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.657266 |
0.440414 |
| accuracy |
0.489498 |
0.440414 |
| precision |
0.489498 |
0.440414 |
| recall |
1 |
0.440414 |
| mcc |
0 |
0.440414 |
Confusion matrix (at threshold=0.440414)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
1750 |
| Labeled as 1 |
0 |
1678 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of Ensemble
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Ensemble structure
| Model |
Weight |
| 5_Default_CatBoost |
1 |
| 6_Default_NeuralNetwork |
1 |
Metric details
|
score |
threshold |
| logloss |
0.601162 |
nan |
| auc |
0.741053 |
nan |
| f1 |
0.705693 |
0.344842 |
| accuracy |
0.677655 |
0.563138 |
| precision |
0.881818 |
0.863075 |
| recall |
1 |
0.0684266 |
| mcc |
0.356771 |
0.563138 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.601162 |
nan |
| auc |
0.741053 |
nan |
| f1 |
0.644809 |
0.563138 |
| accuracy |
0.677655 |
0.563138 |
| precision |
0.69993 |
0.563138 |
| recall |
0.597735 |
0.563138 |
| mcc |
0.356771 |
0.563138 |
Confusion matrix (at threshold=0.563138)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1320 |
430 |
| Labeled as 1 |
675 |
1003 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 2_DecisionTree
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Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
7.0 seconds
Metric details
|
score |
threshold |
| logloss |
0.630016 |
nan |
| auc |
0.691806 |
nan |
| f1 |
0.657266 |
0.152776 |
| accuracy |
0.489498 |
0.152776 |
| precision |
0.489498 |
0.152776 |
| recall |
1 |
0.152776 |
| mcc |
0 |
0.152776 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.630016 |
nan |
| auc |
0.691806 |
nan |
| f1 |
0.657266 |
0.152776 |
| accuracy |
0.489498 |
0.152776 |
| precision |
0.489498 |
0.152776 |
| recall |
1 |
0.152776 |
| mcc |
0 |
0.152776 |
Confusion matrix (at threshold=0.152776)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
1750 |
| Labeled as 1 |
0 |
1678 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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Summary of 3_Default_LightGBM
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LightGBM
- n_jobs: -1
- objective: binary
- num_leaves: 63
- learning_rate: 0.05
- feature_fraction: 0.9
- bagging_fraction: 0.9
- min_data_in_leaf: 10
- metric: auc
- custom_eval_metric_name: None
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
5.3 seconds
Metric details
|
score |
threshold |
| logloss |
0.607881 |
nan |
| auc |
0.72954 |
nan |
| f1 |
0.697651 |
0.395582 |
| accuracy |
0.674154 |
0.489103 |
| precision |
0.841584 |
0.804517 |
| recall |
1 |
0.0624953 |
| mcc |
0.348474 |
0.487772 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.607881 |
nan |
| auc |
0.72954 |
nan |
| f1 |
0.67021 |
0.489103 |
| accuracy |
0.674154 |
0.489103 |
| precision |
0.664131 |
0.489103 |
| recall |
0.6764 |
0.489103 |
| mcc |
0.348325 |
0.489103 |
Confusion matrix (at threshold=0.489103)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1176 |
574 |
| Labeled as 1 |
543 |
1135 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
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Summary of 6_Default_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
3.7 seconds
Metric details
|
score |
threshold |
| logloss |
0.61231 |
nan |
| auc |
0.738246 |
nan |
| f1 |
0.703954 |
0.395631 |
| accuracy |
0.675029 |
0.510942 |
| precision |
0.849741 |
0.892842 |
| recall |
1 |
0.0758362 |
| mcc |
0.354338 |
0.440811 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.61231 |
nan |
| auc |
0.738246 |
nan |
| f1 |
0.676351 |
0.510942 |
| accuracy |
0.675029 |
0.510942 |
| precision |
0.659864 |
0.510942 |
| recall |
0.693683 |
0.510942 |
| mcc |
0.350898 |
0.510942 |
Confusion matrix (at threshold=0.510942)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1150 |
600 |
| Labeled as 1 |
514 |
1164 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 7_Default_RandomForest
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Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
14.0 seconds
Metric details
|
score |
threshold |
| logloss |
0.612224 |
nan |
| auc |
0.725593 |
nan |
| f1 |
0.700493 |
0.35222 |
| accuracy |
0.669195 |
0.458205 |
| precision |
0.823529 |
0.784412 |
| recall |
1 |
0.0992961 |
| mcc |
0.341151 |
0.458205 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.612224 |
nan |
| auc |
0.725593 |
nan |
| f1 |
0.678389 |
0.458205 |
| accuracy |
0.669195 |
0.458205 |
| precision |
0.647186 |
0.458205 |
| recall |
0.712753 |
0.458205 |
| mcc |
0.341151 |
0.458205 |
Confusion matrix (at threshold=0.458205)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1098 |
652 |
| Labeled as 1 |
482 |
1196 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
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Summary of 4_Default_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
29.4 seconds
Metric details
|
score |
threshold |
| logloss |
0.60775 |
nan |
| auc |
0.729286 |
nan |
| f1 |
0.698967 |
0.376195 |
| accuracy |
0.672404 |
0.488988 |
| precision |
0.835443 |
0.837847 |
| recall |
1 |
0.047195 |
| mcc |
0.344713 |
0.488988 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.60775 |
nan |
| auc |
0.729286 |
nan |
| f1 |
0.667456 |
0.488988 |
| accuracy |
0.672404 |
0.488988 |
| precision |
0.663331 |
0.488988 |
| recall |
0.671633 |
0.488988 |
| mcc |
0.344713 |
0.488988 |
Confusion matrix (at threshold=0.488988)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1178 |
572 |
| Labeled as 1 |
551 |
1127 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
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